Langevin
term_id: langevin
Category: basic_concepts
Definition
Langevin dynamics incorporates random noise and damping forces to explore energy landscapes efficiently. In AI, it is primarily used in sampling methods like Hamiltonian Monte Carlo or Stochastic Gradient Langevin Dynamics (SGLD) for Bayesian inference. It helps avoid local minima in optimization by introducing controlled randomness, ensuring better convergence in complex probabilistic models.
Summary
Langevin refers to stochastic differential equations, specifically Langevin dynamics, used to sample from probability distributions by simulating physical motion with friction and noise.
Key Concepts
- Stochastic Differential Equations
- Sampling
- Bayesian Inference
- Noise Injection
Use Cases
- Bayesian neural networks
- MCMC sampling
- Optimization in high-dimensional spaces